Data mining is a technique by which patterns may be identified in seemingly unstructured data. This data can be any type of data, for example, data mining is often used in the medical field so that information associated with a single patient, or group of patients, may be located in existing databases of unstructured data. Data mining techniques are discussed in, for example, U.S. Pat. Nos. 6,112,194; 7,539,927; 7,594,889; 7,627,620; and 7,752,057, the disclosures of which are hereby incorporated herein by reference as if set forth in their entirety.
As discussed above, one area where there is an ever increasing need to identify patterns in unstructured data is in the medical field. Medical data exists in various forms, for example, patient histories and demographic data, clinical and lab results, images (computed tomography (CT) scans, ultrasounds, magnetic resonance imaging (MRI), positron emission tomography (PET) scans and the like), billing information and insurance codes. Just imaging systems and assays alone produce a tremendous amount of relatively unstructured data. As discussed above, many conventional data mining techniques are available to locate patterns in this vast amount of unstructured data so that more accurate diagnoses may be provided and more subtle markers of disease and disease progression may be identified.